Fang Chu

421 citations
16 papers · 286 · h-index 6

Impact in

Papers in

Fang Chu

15 papers receiving 280 citations

Peers

Fang Chu
Comparison fields: 5 of 69
  • Clinical Psychology 139
  • Statistics and Probability 28
  • Statistics, Probability and Uncertainty 23
  • General Health Professions 45
  • Management Science and Operations Research 21
Replace Seong-Ju Kim with:
Seong-Ju Kim South Korea
Abiodun A. Opanuga Nigeria
Anatol‐Fiete Näher Germany
Roy T. Sabo United States
Terrance D. Savitsky United States
Diego Andrés Pérez Ruiz United Kingdom
James Topping United States
Jessica Ritchie United States
Balkishan Sharma India
Diane M. Korngiebel United States
Fang Chu relative to Seong-Ju Kim South Korea Seong-Ju Kim's profile →
Citations per field
00.5×7.5×
Seong-Ju Kim · 1×
Citations per year

Countries citing papers authored by Fang Chu

Since Specialization
Citations

This map shows the geographic impact of Fang Chu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fang Chu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fang Chu more than expected).

Fields of papers citing papers by Fang Chu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fang Chu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fang Chu. The network helps show where Fang Chu may publish in the future.

Co-authors

The 25 scholars most cited alongside Fang Chu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Fang Chu Line = papers co-authored together Fang Chu links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 2017183
2 201240
3 202313
4 200410
5 20228
6 20218
7 20215
8 20135
9 20224
10 20163
11 20222
12 20102
13 20101
14 20181
15
Variance reduction techniques for estimating quantiles and value-at-risk
20101
16 20250

About Fang Chu

Fang Chu is a scholar working on Epidemiology, Management Science and Operations Research, Statistics and Probability, Hepatology and Finance, having authored 16 papers that have together received 286 indexed citations. Recurring topics across this work include Hepatitis B Virus Studies (4 papers), Liver Disease Diagnosis and Treatment (4 papers), Statistical Distribution Estimation and Applications (3 papers), Probability and Risk Models (3 papers), Probabilistic and Robust Engineering Design (2 papers), Hepatitis C virus research (2 papers), Financial Risk and Volatility Modeling (2 papers) and Advanced Clustering Algorithms Research (1 paper). The work is most often cited by research in Clinical Psychology (139 citations), Statistics and Probability (28 citations), Statistics, Probability and Uncertainty (23 citations), General Health Professions (45 citations) and Management Science and Operations Research (21 citations). Fang Chu has collaborated with scholars based in China, United States and Sierra Leone. Frequent co-authors include Marvin K. Nakayama, Dong Ji, Xiao-Xia Niu, Xuezhang Duan, Yingjie Ji, Wengang Li, Huijuan Duan, Zhiqiang Sun, Hongmei Tang and Jin Li. Their work appears in journals such as ACM Transactions on Modeling and Computer Simulation, iScience, Oncotarget, Risk Management and Healthcare Policy and International Journal of Biological Sciences.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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